class_docstr: YOLOV3Head Paper link: https://arxiv.org/abs/1804.02767.
  
  Args:
      num_classes (int): The number of object classes (w/o background)
      in_channels (Sequence[int]): Number of input channels per scale.
      out_channels (Sequence[int]): The number of output channels per scale
          before the final 1x1 layer. Default: (1024, 512, 256).
      anchor_generator (:obj:`ConfigDict` or dict): Config dict for anchor
          generator.
      bbox_coder (:obj:`ConfigDict` or dict): Config of bounding box coder.
      featmap_strides (Sequence[int]): The stride of each scale.
          Should be in descending order. Defaults to (32, 16, 8).
      one_hot_smoother (float): Set a non-zero value to enable label-smooth
          Defaults to 0.
      conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for
          convolution layer. Defaults to None.
      norm_cfg (:obj:`ConfigDict` or dict): Dictionary to construct and
          config norm layer. Defaults to dict(type='BN', requires_grad=True).
      act_cfg (:obj:`ConfigDict` or dict): Config dict for activation layer.
          Defaults to dict(type='LeakyReLU', negative_slope=0.1).
      loss_cls (:obj:`ConfigDict` or dict): Config of classification loss.
      loss_conf (:obj:`ConfigDict` or dict): Config of confidence loss.
      loss_xy (:obj:`ConfigDict` or dict): Config of xy coordinate loss.
      loss_wh (:obj:`ConfigDict` or dict): Config of wh coordinate loss.
      train_cfg (:obj:`ConfigDict` or dict, optional): Training config of
          YOLOV3 head. Defaults to None.
      test_cfg (:obj:`ConfigDict` or dict, optional): Testing config of
          YOLOV3 head. Defaults to None.
docstr: Compute target maps for anchors in multiple images.
  
  Args:
      anchor_list (list[list[Tensor]]): Multi level anchors of each
          image. The outer list indicates images, and the inner list
          corresponds to feature levels of the image. Each element of
          the inner list is a tensor of shape (num_total_anchors, 4).
      responsible_flag_list (list[list[Tensor]]): Multi level responsible
          flags of each image. Each element is a tensor of shape
          (num_total_anchors, )
      batch_gt_instances (list[:obj:`InstanceData`]): Batch of
          gt_instance. It usually includes ``bboxes`` and ``labels``
          attributes.
  
  Returns:
      tuple: Usually returns a tuple containing learning targets.
          - target_map_list (list[Tensor]): Target map of each level.
          - neg_map_list (list[Tensor]): Negative map of each level.
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code: def get_targets(
      self,
      anchor_list: List[List[Tensor]],
      responsible_flag_list: List[List[Tensor]],
      batch_gt_instances: List[InstanceData],
  ) -> tuple:
      """Compute target maps for anchors in multiple images.
  
      Args:
          anchor_list (list[list[Tensor]]): Multi level anchors of each
              image. The outer list indicates images, and the inner list
              corresponds to feature levels of the image. Each element of
              the inner list is a tensor of shape (num_total_anchors, 4).
          responsible_flag_list (list[list[Tensor]]): Multi level responsible
              flags of each image. Each element is a tensor of shape
              (num_total_anchors, )
          batch_gt_instances (list[:obj:`InstanceData`]): Batch of
              gt_instance. It usually includes ``bboxes`` and ``labels``
              attributes.
  
      Returns:
          tuple: Usually returns a tuple containing learning targets.
              - target_map_list (list[Tensor]): Target map of each level.
              - neg_map_list (list[Tensor]): Negative map of each level.
      """
      num_imgs = len(anchor_list)
  
      # anchor number of multi levels
      num_level_anchors = [
          anchors.size(0) for anchors in anchor_list[0]
      ]
  
      results = multi_apply(
          self._get_targets_single,
          anchor_list,
          responsible_flag_list,
          batch_gt_instances,
      )
  
      all_target_maps, all_neg_maps = results
      assert num_imgs == len(all_target_maps) == len(all_neg_maps)
      target_maps_list = images_to_levels(
          all_target_maps, num_level_anchors
      )
      neg_maps_list = images_to_levels(all_neg_maps, num_level_anchors)
  
      return target_maps_list, neg_maps_list
  
imports: import copy
  import warnings
  from typing import List, Optional, Sequence, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule, is_norm
  from mmengine.model import bias_init_with_prob, constant_init, normal_init
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.utils import ConfigType, InstanceList, OptConfigType, OptInstanceList
  from ..task_modules.samplers import PseudoSampler
  from ..utils import filter_scores_and_topk, images_to_levels, multi_apply
  from .base_dense_head import BaseDenseHead
label_desc: Use image node_id 10 for calling the YOLOV3Head obj's underlying member method code with example usage: obj.get_targets(anchor_list, responsible_flag_list, batch_gt_instances) and returns: target_maps_list,  neg_maps_list